This analysis explores the carbon implications of different PV sustainability/circular economy designs in the context of achieving energy transition. These calculations build upon previous work that can be found in journals 13 and 17.
Attempt 1
import numpy as np
import pandas as pd
import os,sys
from pathlib import Path
import matplotlib.pyplot as plt
cwd = os.getcwd() #grabs current working directory
testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'CarbonAnalysis')
inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
carbonfolder = str(Path().resolve().parent.parent / 'PV_ICE'/ 'baselines'/ 'CarbonLayer')
altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')
energyanalysisfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'EnergyAnalysis')
if not os.path.exists(testfolder):
os.makedirs(testfolder)
from platform import python_version
print(python_version())
3.8.8
import PV_ICE
PV_ICE.__version__
'v0.2.0+520.g4b320f8.dirty'
#https://www.learnui.design/tools/data-color-picker.html#palette
#color pallette - modify here for all graphs below
colorpalette=['#000000', #PV ICE baseline
'#595959', '#7F7F7F', '#A6A6A6', '#D9D9D9', #BAU, 4 grays, perc, shj, topcon, irena
#'#067872','#0aa39e','#09d0cd','#00ffff', #realistic cases (4) teals, perc, shj, topcon, irena
'#0579C1','#C00000','#FFC000', #extreme cases (3) long life, high eff, circular
'#6E30A0','#00B3B5','#10C483', #ambitious modules (5) high eff+ long life, 50 yr perc, recycleSi,
'#97CB3F','#FF7E00' #circular perovskite+life, circular perovkiste+ high eff
]
colormats = ['#00bfbf','#ff7f0e','#1f77be','#2ca02c','#d62728','#9467BD','#8C564B', 'black'] #colors for material plots
import matplotlib as mpl #import matplotlib
from cycler import cycler #import cycler
mpl.rcParams['axes.prop_cycle'] = cycler(color=colorpalette) #reset the default color palette of mpl
plt.rcParams.update({'font.size': 14})
plt.rcParams['figure.figsize'] = (8, 6)
scennames_labels = ['PV_ICE','PERC','SHJ','TOPCon','Low\nQuality',
'Long-Lived','High Eff','Circular',
'High Eff\n+ Long-life','Long-Life\n+ Recycling',
'Recycled-Si\n+ Long-life','Circular\n+ Long-life',
'Circular\n+ High Eff'
]
scennames_labels_flat = ['PV_ICE','PERC','SHJ','TOPCon','Low Quality',
'Long-Lived','High Eff','Circular',
'High Eff + Long-life','Long-Life + Recycling',
'Recycled-Si + Long-life','Circular + Long-life',
'Circular + High Eff'
]
MATERIALS = ['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet']
moduleFile_m = os.path.join(baselinesfolder, 'baseline_modules_mass_US.csv')
moduleFile_e = os.path.join(baselinesfolder, 'baseline_modules_energy.csv')
#load in the simulation from Energy Analysis journal
sim1 = PV_ICE.Simulation.load_Simpickle(filename=r'C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis\sim1.pkl')
sim1.calculateCarbonFlows()
sim1.scenario['r_PERC'].dataOut_c
sim1.scenario['r_PERC'].dataOut_m
| Area | Cumulative_Active_Area | EOL_BadStatus | EOL_Landfill0 | EOL_PATHS | EOL_PG | Effective_Capacity_[W] | Landfill_0_ProjLife | MerchantTail_Area | MerchantTail_[W] | ... | Yearly_Sum_Area_PathsBad | Yearly_Sum_Area_PathsGood | Yearly_Sum_Area_atEOL | Yearly_Sum_Power_EOLby_Degradation | Yearly_Sum_Power_EOLby_Failure | Yearly_Sum_Power_EOLby_ProjectLifetime | Yearly_Sum_Power_PathsBad | Yearly_Sum_Power_PathsGood | Yearly_Sum_Power_atEOL | irradiance_stc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5.237421e+06 | 5.237421e+06 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 7.520622e+08 | 0.000000e+00 | 0.0 | 0.0 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.0 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1070.0 |
| 1 | 2.381558e+06 | 7.618978e+06 | 5.019410e-03 | 2.844333e-02 | 5.019410e-03 | 0.000000e+00 | 1.092986e+09 | 0.000000e+00 | 0.0 | 0.0 | ... | 3.346274e-02 | 0.000000e+00 | 3.346274e-02 | 0.0 | 4.769010e+00 | 0.000000e+00 | 4.769010e+00 | 0.000000e+00 | 4.769010e+00 | 1070.0 |
| 2 | 2.399211e+06 | 1.001819e+07 | 3.883317e-01 | 2.200546e+00 | 3.883317e-01 | 0.000000e+00 | 1.437059e+09 | 0.000000e+00 | 0.0 | 0.0 | ... | 2.588878e+00 | 0.000000e+00 | 2.588878e+00 | 0.0 | 3.662374e+02 | 0.000000e+00 | 3.662374e+02 | 0.000000e+00 | 3.662374e+02 | 1070.0 |
| 3 | 3.636895e+06 | 1.365505e+07 | 4.784439e+00 | 2.711182e+01 | 4.784439e+00 | 0.000000e+00 | 1.965030e+09 | 0.000000e+00 | 0.0 | 0.0 | ... | 3.189626e+01 | 0.000000e+00 | 3.189626e+01 | 0.0 | 4.481373e+03 | 0.000000e+00 | 4.481373e+03 | 0.000000e+00 | 4.481373e+03 | 1070.0 |
| 4 | 7.372109e+06 | 2.102697e+07 | 2.774546e+01 | 1.572243e+02 | 2.774546e+01 | 0.000000e+00 | 3.051979e+09 | 0.000000e+00 | 0.0 | 0.0 | ... | 1.849697e+02 | 0.000000e+00 | 1.849697e+02 | 0.0 | 2.582007e+04 | 0.000000e+00 | 2.582007e+04 | 0.000000e+00 | 2.582007e+04 | 1070.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 96 | 7.591823e+09 | 3.541404e+11 | 1.667387e+08 | 1.552650e+09 | 4.657949e+09 | 4.491210e+09 | 8.508872e+13 | 1.497070e+09 | 0.0 | 0.0 | ... | 2.223183e+08 | 4.491210e+09 | 6.210598e+09 | 0.0 | 4.896471e+10 | 1.297565e+12 | 4.896471e+10 | 9.731736e+11 | 1.346529e+12 | 1070.0 |
| 97 | 8.003568e+09 | 3.554404e+11 | 1.278019e+08 | 1.675878e+09 | 5.027633e+09 | 4.899831e+09 | 8.530804e+13 | 1.633277e+09 | 0.0 | 0.0 | ... | 1.704025e+08 | 4.899831e+09 | 6.703511e+09 | 0.0 | 3.775151e+10 | 1.415620e+12 | 3.775151e+10 | 1.061715e+12 | 1.453372e+12 | 1070.0 |
| 98 | 4.560406e+09 | 3.574687e+11 | 1.455385e+08 | 6.330470e+08 | 1.899141e+09 | 1.753602e+09 | 8.552736e+13 | 5.845342e+08 | 0.0 | 0.0 | ... | 1.940513e+08 | 1.753602e+09 | 2.532188e+09 | 0.0 | 4.301322e+10 | 5.066369e+11 | 4.301322e+10 | 3.799777e+11 | 5.496501e+11 | 1070.0 |
| 99 | 4.619609e+09 | 3.594903e+11 | 1.708910e+08 | 6.494993e+08 | 1.948498e+09 | 1.777607e+09 | 8.574668e+13 | 5.925356e+08 | 0.0 | 0.0 | ... | 2.278547e+08 | 1.777607e+09 | 2.597997e+09 | 0.0 | 5.050157e+10 | 5.135720e+11 | 5.050157e+10 | 3.851790e+11 | 5.640736e+11 | 1070.0 |
| 100 | 4.700811e+09 | 3.615007e+11 | 2.067210e+08 | 6.725818e+08 | 2.017745e+09 | 1.811024e+09 | 8.596600e+13 | 6.036748e+08 | 0.0 | 0.0 | ... | 2.756280e+08 | 1.811024e+09 | 2.690327e+09 | 0.0 | 6.103742e+10 | 5.232268e+11 | 6.103742e+10 | 3.924201e+11 | 5.842642e+11 | 1070.0 |
101 rows × 45 columns
To parallel the PV deployment, we will assume that we globally hit 100% RE in 2050 with the 75 TW of PV. As such, we need to change the future projection of marketshares of the different country grids.
One scenario with decarb grid, one scenario with decarb grid and heat
Estimating that 60-70% generation will be from Solar, 30-40% from wind, and any remainder from "other renewables"
countrygridmix = pd.read_csv(os.path.join(carbonfolder,'baseline_countrygridmix.csv'), index_col='year')
gridsources = ['Bioenergy','Hydro','Nuclear','OtherFossil','OtherRenewables','Solar','Wind']
nonRE = ['Coal','Gas','OtherFossil','Nuclear','Bioenergy']
countrygridmix.loc[2023:,:]=np.nan #delete 2023 to 2050
nonRE_search = '|'.join(nonRE) #create nonRE search
countrygridmix.loc[2050, countrygridmix.columns.str.contains(nonRE_search)] = 0.0 #set all nonRE to 0 in 2050
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Solar')] = 63.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Wind')] = 33.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Hydro')] = 3.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('OtherRenewables')] = 1.0
#numbers derived from leading scenario electricity generation Breyer et al 2022 scenarios (EU focused)
countrygridmix_100RE2050 = countrygridmix.interpolate() #linearly interpolate between 2022 and 2050
apnd_idx = pd.RangeIndex(start=2051,stop=2101,step=1) #create temp df
apnd_df = pd.DataFrame(columns=countrygridmix_100RE2050.columns, index=apnd_idx )
countrygridmix_100RE20502100 = pd.concat([countrygridmix_100RE2050.loc[2000:],apnd_df], axis=0) #extend through 2100
countrygridmix_100RE20502100.ffill(inplace=True) #propogate 2050 values through 2100
countrygridmix_100RE20502100.loc[2050]
China_Bioenergy 0.0
China_Coal 0.0
China_Gas 0.0
China_Hydro 3.0
China_Nuclear 0.0
...
Zambia_Nuclear 0.0
Zambia_OtherFossil 0.0
Zambia_OtherRenewables 1.0
Zambia_Solar 63.0
Zambia_Wind 33.0
Name: 2050, Length: 472, dtype: float64
This is a simple projection, assumes all countries have same ratio of PV and wind (which we know can't be true). Update in future with country specific projections.
pd.read_csv(os.path.join(carbonfolder,'baseline_electricityemissionfactors.csv'), index_col=[0])
| CO2eq_gpWh_IPCC2006 | CO2eq_gpWh_ember | CO2_gpWh_EIA | CO2_gpWh_EPA | |
|---|---|---|---|---|
| Energy Source | ||||
| Bioenergy | 0.3005 | 0.230 | 0.0000 | 0.3170 |
| Coal | 0.3487 | 0.820 | 0.3215 | 0.3380 |
| Gas | 0.2291 | 0.490 | 0.1805 | 0.1810 |
| Hydro | 0.0000 | 0.024 | 0.0000 | 0.0000 |
| Nuclear | 0.0000 | 0.012 | 0.0000 | 0.0000 |
| OtherFossil | 0.2671 | 0.700 | 0.2413 | 0.0000 |
| OtherRenewables | 0.0000 | 0.038 | 0.0000 | 0.0000 |
| Solar | 0.0000 | 0.048 | 0.0000 | 0.0000 |
| Wind | 0.0000 | 0.011 | 0.0000 | 0.0000 |
| SteamAndHeat | 0.0000 | 0.000 | 0.0000 | 0.2266 |
sim1.calculateCarbonFlows(countrygridmixes=countrygridmix_100RE20502100)
>>>> Calculating Carbon Flows <<<< Working on Scenario: PV_ICE ******************** Working on Carbon for Module ==> Working on Carbon for Material : glass ==> Working on Carbon for Material : silicon ==> Working on Carbon for Material : silver ==> Working on Carbon for Material : aluminium_frames ==> Working on Carbon for Material : copper ==> Working on Carbon for Material : encapsulant ==> Working on Carbon for Material : backsheet Working on Scenario: r_PERC ******************** Working on Carbon for Module ==> Working on Carbon for Material : glass ==> Working on Carbon for Material : silicon ==> Working on Carbon for Material : silver ==> Working on Carbon for Material : aluminium_frames ==> Working on Carbon for Material : copper ==> Working on Carbon for Material : encapsulant ==> Working on Carbon for Material : backsheet Working on Scenario: r_SHJ ******************** Working on Carbon for Module ==> Working on Carbon for Material : glass ==> Working on Carbon for Material : silicon ==> Working on Carbon for Material : silver ==> Working on Carbon for Material : aluminium_frames ==> Working on Carbon for Material : copper ==> Working on Carbon for Material : encapsulant ==> Working on Carbon for Material : backsheet Working on Scenario: r_TOPCon ******************** Working on Carbon for Module ==> Working on Carbon for Material : glass ==> Working on Carbon for Material : silicon ==> Working on Carbon for Material : silver ==> Working on Carbon for Material : aluminium_frames ==> Working on Carbon for Material : copper ==> Working on Carbon for Material : encapsulant ==> Working on Carbon for Material : backsheet Working on Scenario: r_IRENA ******************** Working on Carbon for Module ==> Working on Carbon for Material : glass ==> Working on Carbon for Material : silicon ==> Working on Carbon for Material : silver ==> Working on Carbon for Material : aluminium_frames ==> Working on Carbon for Material : copper ==> Working on Carbon for Material : encapsulant ==> Working on Carbon for Material : backsheet Working on Scenario: ex_Life ******************** Working on Carbon for Module ==> Working on Carbon for Material : glass ==> Working on Carbon for Material : silicon ==> Working on Carbon for Material : silver ==> Working on Carbon for Material : aluminium_frames ==> Working on Carbon for Material : copper ==> Working on Carbon for Material : encapsulant ==> Working on Carbon for Material : backsheet Working on Scenario: ex_High_eff ******************** Working on Carbon for Module ==> Working on Carbon for Material : glass ==> Working on Carbon for Material : silicon ==> Working on Carbon for Material : silver ==> Working on Carbon for Material : aluminium_frames ==> Working on Carbon for Material : copper ==> Working on Carbon for Material : encapsulant ==> Working on Carbon for Material : backsheet Working on Scenario: ex_Circular ******************** Working on Carbon for Module ==> Working on Carbon for Material : glass ==> Working on Carbon for Material : silicon ==> Working on Carbon for Material : silver ==> Working on Carbon for Material : aluminium_frames ==> Working on Carbon for Material : copper ==> Working on Carbon for Material : encapsulant ==> Working on Carbon for Material : backsheet Working on Scenario: h_EffLife ******************** Working on Carbon for Module ==> Working on Carbon for Material : glass ==> Working on Carbon for Material : silicon ==> Working on Carbon for Material : silver ==> Working on Carbon for Material : aluminium_frames ==> Working on Carbon for Material : copper ==> Working on Carbon for Material : encapsulant ==> Working on Carbon for Material : backsheet Working on Scenario: h_50PERC ******************** Working on Carbon for Module ==> Working on Carbon for Material : glass ==> Working on Carbon for Material : silicon ==> Working on Carbon for Material : silver ==> Working on Carbon for Material : aluminium_frames ==> Working on Carbon for Material : copper ==> Working on Carbon for Material : encapsulant ==> Working on Carbon for Material : backsheet Working on Scenario: h_RecycledPERC ******************** Working on Carbon for Module ==> Working on Carbon for Material : glass ==> Working on Carbon for Material : silicon ==> Working on Carbon for Material : silver ==> Working on Carbon for Material : aluminium_frames ==> Working on Carbon for Material : copper ==> Working on Carbon for Material : encapsulant ==> Working on Carbon for Material : backsheet Working on Scenario: h_Perovskite_life ******************** Working on Carbon for Module ==> Working on Carbon for Material : glass ==> Working on Carbon for Material : silicon ==> Working on Carbon for Material : silver ==> Working on Carbon for Material : aluminium_frames ==> Working on Carbon for Material : copper ==> Working on Carbon for Material : encapsulant ==> Working on Carbon for Material : backsheet Working on Scenario: h_Perovskite_Eff ******************** Working on Carbon for Module ==> Working on Carbon for Material : glass ==> Working on Carbon for Material : silicon ==> Working on Carbon for Material : silver ==> Working on Carbon for Material : aluminium_frames ==> Working on Carbon for Material : copper ==> Working on Carbon for Material : encapsulant ==> Working on Carbon for Material : backsheet
this will become the aggregate carbon results function
scenarios = sim1.scenario
sim_carbon_results = pd.DataFrame()
sim_annual_carbon = pd.DataFrame()
for scen in scenarios:
print(scen)
mod_carbon_scen_results = sim1.scenario[scen].dataOut_c.add_prefix(str(scen+'_'))
#mod annual carbon calcs here (selecting to avoid double counting)
mod_mfg_carbon_total = mod_carbon_scen_results.filter(like='Global_gCO2eqpwh_mod_MFG_gCO2eq') #annual mfging carbon
mod_nonvMFG = ['Install','OandM','Repair','Demount','Store','Resell','ReMFG','Recycle'] #could remove from loop
nonvMFG_search = '|'.join(mod_nonvMFG) #create nonRE search
mod_carbon_sum_nonvmfg = mod_carbon_scen_results.loc[:,mod_carbon_scen_results.columns.str.contains(nonvMFG_search)] #annual non mfging carbon
scen_annual_carbon_mod = pd.concat([mod_mfg_carbon_total,mod_carbon_sum_nonvmfg], axis=1)
scen_annual_carbon_mod[scen+'_Annual_Emit_mod_gCO2eq'] = scen_annual_carbon_mod.sum(axis=1)
scenmatdc = pd.DataFrame()
for mat in MATERIALS:
print(mat)
mat_carbon_scen_results = sim1.scenario[scen].material[mat].matdataOut_c.add_prefix(str(scen+'_'+mat+'_'))
#calculation for annual carbon emissions total (selecting to avoid double countings)
mat_vmfg_total = mat_carbon_scen_results.filter(like='vMFG_total')
mat_ce_recycle = mat_carbon_scen_results.filter(like='Recycle_e_p')
mat_ce_remfg = mat_carbon_scen_results.filter(like='ReMFG_clean')
mat_landfill = mat_carbon_scen_results.filter(like='landfill_total')
mat_scen_annual_carbon = pd.concat([mat_vmfg_total,mat_ce_recycle,mat_ce_remfg,mat_landfill], axis=1)
mat_scen_annual_carbon[scen+'_Annual_Emit_'+mat+'_gCO2eq'] = mat_scen_annual_carbon.sum(axis=1)
scenmatdc = pd.concat([scenmatdc,mat_carbon_scen_results,
mat_scen_annual_carbon[scen+'_Annual_Emit_'+mat+'_gCO2eq']], axis=1) #group all material dc
scen_carbon_results = pd.concat([mod_carbon_scen_results,scenmatdc], axis=1) #append mats to mod
sim_carbon_results = pd.concat([sim_carbon_results, scen_carbon_results], axis=1) #append all scens "raw" data
#calculate annual carbon emits with grouping by mod and mat
scen_mats_annual_carbon = scenmatdc.filter(like='Annual_Emit')
scen_mod_annual_carbon = scen_annual_carbon_mod.filter(like='Annual_Emit_mod')
scen_annual_carbon = pd.concat([scen_mod_annual_carbon,scen_mats_annual_carbon], axis=1)
scen_annual_carbon[scen+'_Annual_Emit_total_modmats_gCO2eq'] = scen_annual_carbon.sum(axis=1)
sim_annual_carbon = pd.concat([sim_annual_carbon,scen_annual_carbon], axis=1)
#FIX INDEX of dfs
sim_annual_carbon.index = pd.RangeIndex(start=2000,stop=2101,step=1)
sim_carbon_results.index = pd.RangeIndex(start=2000,stop=2101,step=1)
#return sim_carbon_results, sim_annual_carbon
PV_ICE glass silicon silver aluminium_frames copper encapsulant backsheet r_PERC glass silicon silver aluminium_frames copper encapsulant backsheet r_SHJ glass silicon silver aluminium_frames copper encapsulant backsheet r_TOPCon glass silicon silver aluminium_frames copper encapsulant backsheet r_IRENA glass silicon silver aluminium_frames copper encapsulant backsheet ex_Life glass silicon silver aluminium_frames copper encapsulant backsheet ex_High_eff glass silicon silver aluminium_frames copper encapsulant backsheet ex_Circular glass silicon silver aluminium_frames copper encapsulant backsheet h_EffLife glass silicon silver aluminium_frames copper encapsulant backsheet h_50PERC glass silicon silver aluminium_frames copper encapsulant backsheet h_RecycledPERC glass silicon silver aluminium_frames copper encapsulant backsheet h_Perovskite_life glass silicon silver aluminium_frames copper encapsulant backsheet h_Perovskite_Eff glass silicon silver aluminium_frames copper encapsulant backsheet
sim_carbon_results
| PV_ICE_China_mod_MFG_gCO2eq | PV_ICE_India_mod_MFG_gCO2eq | PV_ICE_Taiwan_mod_MFG_gCO2eq | PV_ICE_Germany_mod_MFG_gCO2eq | PV_ICE_Japan_mod_MFG_gCO2eq | PV_ICE_SKorea_mod_MFG_gCO2eq | PV_ICE_Canada_mod_MFG_gCO2eq | PV_ICE_Mexico_mod_MFG_gCO2eq | PV_ICE_USA_mod_MFG_gCO2eq | PV_ICE_VietNam_mod_MFG_gCO2eq | ... | h_Perovskite_Eff_backsheet_mat_LQeol_p_gCO2eq | h_Perovskite_Eff_backsheet_mat_LQ_p_gCO2eq | h_Perovskite_Eff_backsheet_mat_HQmfg_p_gCO2eq | h_Perovskite_Eff_backsheet_mat_HQeol_p_gCO2eq | h_Perovskite_Eff_backsheet_mat_HQ_p_gCO2eq | h_Perovskite_Eff_backsheet_mat_vMFG_energy_gCO2eq | h_Perovskite_Eff_backsheet_mat_vMFG_total_gCO2eq | h_Perovskite_Eff_backsheet_mat_Recycle_e_p_gCO2eq | h_Perovskite_Eff_backsheet_mat_landfill_total_gCO2eq | h_Perovskite_Eff_Annual_Emit_backsheet_gCO2eq | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2000 | 6.287490e+09 | 3.726969e+09 | 6.318297e+08 | 1.794525e+09 | 0.0 | 0.0 | 5.414536e+08 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.919551e+09 | 1.445189e+10 | 0.0 | 2.391377e+06 | 1.445429e+10 |
| 2001 | 2.191320e+09 | 1.349368e+09 | 2.332168e+08 | 6.378740e+08 | 0.0 | 0.0 | 2.025203e+08 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.426169e+09 | 5.271991e+09 | 0.0 | 8.790758e+05 | 5.272870e+09 |
| 2002 | 2.698970e+09 | 1.657363e+09 | 2.845645e+08 | 7.803337e+08 | 0.0 | 0.0 | 2.361252e+08 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.772188e+09 | 6.533002e+09 | 0.0 | 1.081372e+06 | 6.534083e+09 |
| 2003 | 4.231349e+09 | 2.545516e+09 | 4.447166e+08 | 1.210860e+09 | 0.0 | 0.0 | 3.712389e+08 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.775669e+09 | 1.019997e+10 | 0.0 | 1.727943e+06 | 1.020169e+10 |
| 2004 | 8.532618e+09 | 4.972363e+09 | 9.090725e+08 | 2.421317e+09 | 0.0 | 0.0 | 7.039480e+08 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5.751251e+09 | 2.120316e+10 | 0.0 | 3.626892e+06 | 2.120679e+10 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2096 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.0 | 0.0 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000e+00 | 0.000000e+00 | 0.0 | 0.000000e+00 | 0.000000e+00 |
| 2097 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.0 | 0.0 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000e+00 | 0.000000e+00 | 0.0 | 0.000000e+00 | 0.000000e+00 |
| 2098 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.0 | 0.0 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000e+00 | 0.000000e+00 | 0.0 | 0.000000e+00 | 0.000000e+00 |
| 2099 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.0 | 0.0 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000e+00 | 0.000000e+00 | 0.0 | 0.000000e+00 | 0.000000e+00 |
| 2100 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.0 | 0.0 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000e+00 | 0.000000e+00 | 0.0 | 0.000000e+00 | 0.000000e+00 |
101 rows × 3809 columns
for scen in scenarios:
scen_annual_carbon = sim_annual_carbon.filter(like='Annual_Emit').filter(like=scen)/1e12 #million tonnes
plt.plot([],[],color=colormats[0], label=MATERIALS[0])
plt.plot([],[],color=colormats[1], label=MATERIALS[1])
plt.plot([],[],color=colormats[2], label=MATERIALS[2])
plt.plot([],[],color=colormats[3], label=MATERIALS[3])
plt.plot([],[],color=colormats[4], label=MATERIALS[4])
plt.plot([],[],color=colormats[5], label=MATERIALS[5])
plt.plot([],[],color=colormats[6], label=MATERIALS[6])
plt.plot([],[],color=colormats[7], label='module')
plt.stackplot(scen_annual_carbon.index,
scen_annual_carbon[scen+'_Annual_Emit_glass_gCO2eq'],
scen_annual_carbon[scen+'_Annual_Emit_silicon_gCO2eq'],
scen_annual_carbon[scen+'_Annual_Emit_silver_gCO2eq'],
scen_annual_carbon[scen+'_Annual_Emit_aluminium_frames_gCO2eq'],
scen_annual_carbon[scen+'_Annual_Emit_copper_gCO2eq'],
scen_annual_carbon[scen+'_Annual_Emit_encapsulant_gCO2eq'],
scen_annual_carbon[scen+'_Annual_Emit_backsheet_gCO2eq'],
scen_annual_carbon[scen+'_Annual_Emit_mod_gCO2eq'],
colors = colormats)
plt.title(scen+':\nGHG Emissions Annually by Module and Material Lifecycle')
plt.ylabel('GHG Emissions Annually from Lifecycle Mats and Mods\n[million metric tonnes CO2eq]')
plt.xlim(2000,2100)
handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.4,1))
#plt.legend()
plt.show()
sim_cumu_carbon = sim_annual_carbon.cumsum()
maxy = round(sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats').max()/1e12,-3)
sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats')
PV_ICE_Annual_Emit_total_modmats_gCO2eq 2.963565e+16 r_PERC_Annual_Emit_total_modmats_gCO2eq 2.126845e+16 r_SHJ_Annual_Emit_total_modmats_gCO2eq 2.024885e+16 r_TOPCon_Annual_Emit_total_modmats_gCO2eq 2.067584e+16 r_IRENA_Annual_Emit_total_modmats_gCO2eq 3.050720e+16 ex_Life_Annual_Emit_total_modmats_gCO2eq 2.274670e+16 ex_High_eff_Annual_Emit_total_modmats_gCO2eq 3.053505e+16 ex_Circular_Annual_Emit_total_modmats_gCO2eq 2.721381e+16 h_EffLife_Annual_Emit_total_modmats_gCO2eq 2.295700e+16 h_50PERC_Annual_Emit_total_modmats_gCO2eq 2.264909e+16 h_RecycledPERC_Annual_Emit_total_modmats_gCO2eq 2.254946e+16 h_Perovskite_life_Annual_Emit_total_modmats_gCO2eq 2.653388e+16 h_Perovskite_Eff_Annual_Emit_total_modmats_gCO2eq 2.293411e+16 Name: 2100, dtype: float64
#colormats = ['#00bfbf','#ff7f0e','#1f77be','#2ca02c','#d62728','#9467BD','#8C564B','black'] #colors for material plots
for scen in scenarios:
scen_cumu_carbon = sim_cumu_carbon.filter(like='Annual_Emit').filter(like=scen)/1e12 #million tonnes
plt.plot([],[],color=colormats[0], label=MATERIALS[0])
plt.plot([],[],color=colormats[1], label=MATERIALS[1])
plt.plot([],[],color=colormats[2], label=MATERIALS[2])
plt.plot([],[],color=colormats[3], label=MATERIALS[3])
plt.plot([],[],color=colormats[4], label=MATERIALS[4])
plt.plot([],[],color=colormats[5], label=MATERIALS[5])
plt.plot([],[],color=colormats[6], label=MATERIALS[6])
plt.plot([],[],color=colormats[7], label='module')
plt.stackplot(scen_cumu_carbon.index,
scen_cumu_carbon[scen+'_Annual_Emit_glass_gCO2eq'],
scen_cumu_carbon[scen+'_Annual_Emit_silicon_gCO2eq'],
scen_cumu_carbon[scen+'_Annual_Emit_silver_gCO2eq'],
scen_cumu_carbon[scen+'_Annual_Emit_aluminium_frames_gCO2eq'],
scen_cumu_carbon[scen+'_Annual_Emit_copper_gCO2eq'],
scen_cumu_carbon[scen+'_Annual_Emit_encapsulant_gCO2eq'],
scen_cumu_carbon[scen+'_Annual_Emit_backsheet_gCO2eq'],
scen_cumu_carbon[scen+'_Annual_Emit_mod_gCO2eq'],
colors = colormats)
plt.title(scen+':\nGHG Emissions Annually by Module and Material Lifecycle')
plt.ylabel('GHG Emissions Annually from Lifecycle Mats and Mods\n[million metric tonnes CO2eq]')
plt.xlim(2000,2100)
plt.ylim(0,maxy)
handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.4,1))
#plt.legend()
plt.show()
sim_cumu_carbon
| PV_ICE_Annual_Emit_mod_gCO2eq | PV_ICE_Annual_Emit_glass_gCO2eq | PV_ICE_Annual_Emit_silicon_gCO2eq | PV_ICE_Annual_Emit_silver_gCO2eq | PV_ICE_Annual_Emit_aluminium_frames_gCO2eq | PV_ICE_Annual_Emit_copper_gCO2eq | PV_ICE_Annual_Emit_encapsulant_gCO2eq | PV_ICE_Annual_Emit_backsheet_gCO2eq | PV_ICE_Annual_Emit_total_modmats_gCO2eq | r_PERC_Annual_Emit_mod_gCO2eq | ... | h_Perovskite_life_Annual_Emit_total_modmats_gCO2eq | h_Perovskite_Eff_Annual_Emit_mod_gCO2eq | h_Perovskite_Eff_Annual_Emit_glass_gCO2eq | h_Perovskite_Eff_Annual_Emit_silicon_gCO2eq | h_Perovskite_Eff_Annual_Emit_silver_gCO2eq | h_Perovskite_Eff_Annual_Emit_aluminium_frames_gCO2eq | h_Perovskite_Eff_Annual_Emit_copper_gCO2eq | h_Perovskite_Eff_Annual_Emit_encapsulant_gCO2eq | h_Perovskite_Eff_Annual_Emit_backsheet_gCO2eq | h_Perovskite_Eff_Annual_Emit_total_modmats_gCO2eq | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2000 | 1.302635e+10 | 7.926511e+10 | 9.851568e+11 | 1.806152e+10 | 2.951140e+11 | 1.095485e+08 | 3.144897e+10 | 1.445429e+10 | 1.436637e+12 | 1.132197e+10 | ... | 1.436637e+12 | 1.302635e+10 | 7.926511e+10 | 9.851568e+11 | 1.806152e+10 | 2.951140e+11 | 1.095485e+08 | 3.144897e+10 | 1.445429e+10 | 1.436637e+12 |
| 2001 | 1.765591e+10 | 1.071198e+11 | 1.297904e+12 | 2.408112e+10 | 3.999794e+11 | 1.466912e+08 | 4.262252e+10 | 1.972716e+10 | 1.909237e+12 | 1.648577e+10 | ... | 1.909237e+12 | 1.765591e+10 | 1.071198e+11 | 1.297904e+12 | 2.408112e+10 | 3.999794e+11 | 1.466912e+08 | 4.262252e+10 | 1.972716e+10 | 1.909237e+12 |
| 2002 | 2.333327e+10 | 1.403331e+11 | 1.611551e+12 | 3.098442e+10 | 5.248225e+11 | 1.912913e+08 | 5.610222e+10 | 2.626124e+10 | 2.413579e+12 | 2.179171e+10 | ... | 2.413579e+12 | 2.333327e+10 | 1.403331e+11 | 1.611551e+12 | 3.098442e+10 | 5.248225e+11 | 1.912913e+08 | 5.610222e+10 | 2.626124e+10 | 2.413579e+12 |
| 2003 | 3.216826e+10 | 1.903309e+11 | 2.003080e+12 | 4.102774e+10 | 7.171775e+11 | 2.602262e+08 | 7.661591e+10 | 3.646293e+10 | 3.097123e+12 | 3.004872e+10 | ... | 3.097123e+12 | 3.216826e+10 | 1.903309e+11 | 2.003080e+12 | 4.102774e+10 | 7.171775e+11 | 2.602262e+08 | 7.661591e+10 | 3.646293e+10 | 3.097123e+12 |
| 2004 | 4.977404e+10 | 2.906310e+11 | 2.656899e+12 | 6.048733e+10 | 1.094709e+12 | 4.034545e+08 | 1.182876e+11 | 5.766972e+10 | 4.328861e+12 | 4.650269e+10 | ... | 4.328861e+12 | 4.977404e+10 | 2.906310e+11 | 2.656899e+12 | 6.048733e+10 | 1.094709e+12 | 4.034545e+08 | 1.182876e+11 | 5.766972e+10 | 4.328861e+12 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2096 | 8.661511e+13 | 9.681365e+15 | 6.186923e+15 | 3.830767e+13 | 7.434280e+15 | 8.232011e+12 | 2.882390e+15 | 2.116173e+15 | 2.843429e+16 | 7.955485e+13 | ... | 2.588437e+16 | 1.055897e+14 | 6.725123e+15 | 5.651124e+15 | 6.575802e+13 | 4.077729e+15 | 1.502691e+13 | 5.546502e+15 | 2.325043e+13 | 2.221010e+16 |
| 2097 | 8.661511e+13 | 9.782482e+15 | 6.233863e+15 | 3.859118e+13 | 7.507335e+15 | 8.305166e+12 | 2.912165e+15 | 2.137900e+15 | 2.870726e+16 | 7.955485e+13 | ... | 2.604102e+16 | 1.055897e+14 | 6.765008e+15 | 5.690372e+15 | 6.651118e+13 | 4.099235e+15 | 1.522125e+13 | 5.625601e+15 | 2.325043e+13 | 2.239079e+16 |
| 2098 | 8.661511e+13 | 9.890074e+15 | 6.283796e+15 | 3.889277e+13 | 7.585002e+15 | 8.382985e+12 | 2.943838e+15 | 2.161013e+15 | 2.899762e+16 | 7.955485e+13 | ... | 2.619843e+16 | 1.055897e+14 | 6.803572e+15 | 5.730015e+15 | 6.728376e+13 | 4.119829e+15 | 1.542060e+13 | 5.706738e+15 | 2.325043e+13 | 2.257170e+16 |
| 2099 | 8.661511e+13 | 1.000423e+16 | 6.336764e+15 | 3.921269e+13 | 7.667346e+15 | 8.465533e+12 | 2.977437e+15 | 2.185530e+15 | 2.930560e+16 | 7.955485e+13 | ... | 2.635667e+16 | 1.055897e+14 | 6.840671e+15 | 5.770083e+15 | 6.807751e+13 | 4.139413e+15 | 1.562540e+13 | 5.790099e+15 | 2.325043e+13 | 2.275281e+16 |
| 2100 | 8.661511e+13 | 1.012661e+16 | 6.393529e+15 | 3.955555e+13 | 7.755542e+15 | 8.553999e+12 | 3.013444e+15 | 2.211805e+15 | 2.963565e+16 | 7.955485e+13 | ... | 2.653388e+16 | 1.055897e+14 | 6.876225e+15 | 5.810594e+15 | 6.889341e+13 | 4.157934e+15 | 1.583593e+13 | 5.875786e+15 | 2.325043e+13 | 2.293411e+16 |
101 rows × 117 columns
#create a df from which to do a bar chart of 2100 emissions by mat/mod
mats_emit_2100 = pd.DataFrame() #index=scennames_labels_flat
for mat in MATERIALS:
mat_emit_2100 = pd.Series(sim_cumu_carbon.loc[2100].filter(like=mat).values)
mats_emit_2100 = pd.concat([mats_emit_2100, mat_emit_2100], axis=1)
mats_emit_2100
mats_emit_2100.columns = MATERIALS
modmats_emit_2100 = pd.concat([mats_emit_2100,pd.Series(sim_cumu_carbon.loc[2100].filter(like='mod_').values)], axis=1)
modmats_emit_2100.index = scennames_labels_flat
modmats_emit_2100.rename(columns={0:'module'}, inplace=True)
modmats_emit_2100_megatonne = modmats_emit_2100/1e12
modmats_emit_2100_megatonne
| glass | silicon | silver | aluminium_frames | copper | encapsulant | backsheet | module | |
|---|---|---|---|---|---|---|---|---|
| PV_ICE | 10126.610037 | 6393.528822 | 39.555545 | 7755.542240 | 8.553999 | 3013.443849 | 2211.805161 | 86.615111 |
| PERC | 8762.994612 | 4692.866948 | 25.191311 | 4878.062693 | 7.933845 | 2800.113453 | 21.729228 | 79.554845 |
| SHJ | 8331.662313 | 4465.729982 | 44.794737 | 4639.401770 | 7.545504 | 2662.628153 | 21.330504 | 75.758903 |
| TOPCon | 8509.280286 | 4559.621644 | 43.333633 | 4737.762881 | 7.705695 | 2719.277076 | 21.528020 | 77.335447 |
| Low Quality | 10920.089504 | 4341.113961 | 40.635431 | 6893.787669 | 12.970040 | 4731.060652 | 3466.044108 | 101.496270 |
| Long-Lived | 9270.300486 | 5078.154692 | 29.471217 | 6013.561653 | 6.784228 | 2244.838020 | 22.463092 | 81.124534 |
| High Eff | 12963.265245 | 6145.955317 | 38.732726 | 8176.783647 | 8.755677 | 3115.456836 | 21.291503 | 64.810392 |
| Circular | 8977.849914 | 4281.988316 | 92.251404 | 5569.326850 | 21.597082 | 8123.698405 | 23.250434 | 123.842906 |
| High Eff + Long-life | 9507.951929 | 4926.966042 | 31.290604 | 6098.983123 | 6.741955 | 2295.131175 | 21.291435 | 68.646424 |
| Long-Life + Recycling | 9400.037004 | 5075.691113 | 32.719877 | 5668.840797 | 6.882528 | 2360.827092 | 22.463337 | 81.633002 |
| Recycled-Si + Long-life | 8218.255683 | 2257.178941 | 33.593199 | 4716.342625 | 11.277104 | 4044.829561 | 2965.494878 | 302.484496 |
| Circular + Long-life | 8836.160604 | 6431.136887 | 66.818549 | 5517.225262 | 15.130176 | 5526.821345 | 23.250434 | 117.337425 |
| Circular + High Eff | 6876.225455 | 5810.593695 | 68.893415 | 4157.934259 | 15.835931 | 5875.785931 | 23.250434 | 105.589706 |
fig_cumuemit_modmat, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True,
gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['glass'], color=colormats[0])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['silicon'],
bottom=modmats_emit_2100_megatonne[0:5]['glass'], color=colormats[1])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['silver'],
bottom=modmats_emit_2100_megatonne.iloc[0:5,0:2].sum(axis=1), color=colormats[2])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['aluminium_frames'],
bottom=modmats_emit_2100_megatonne.iloc[0:5,0:3].sum(axis=1), color=colormats[3])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['copper'],
bottom=modmats_emit_2100_megatonne.iloc[0:5,0:4].sum(axis=1), color=colormats[4])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['encapsulant'],
bottom=modmats_emit_2100_megatonne.iloc[0:5,0:5].sum(axis=1), color=colormats[5])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['backsheet'],
bottom=modmats_emit_2100_megatonne.iloc[0:5,0:6].sum(axis=1), color=colormats[6])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['module'],
bottom=modmats_emit_2100_megatonne.iloc[0:5,0:7].sum(axis=1), color='black')
ax0.set_ylim(0,31000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes CO2eq]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.9', ls='--')
ax0.set_axisbelow(True)
#Extreme
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['glass'], color=colormats[0])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['silicon'],
bottom=modmats_emit_2100_megatonne[5:8]['glass'], color=colormats[1])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['silver'],
bottom=modmats_emit_2100_megatonne.iloc[5:8,0:2].sum(axis=1), color=colormats[2])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['aluminium_frames'],
bottom=modmats_emit_2100_megatonne.iloc[5:8,0:3].sum(axis=1), color=colormats[3])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['copper'],
bottom=modmats_emit_2100_megatonne.iloc[5:8,0:4].sum(axis=1), color=colormats[4])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['encapsulant'],
bottom=modmats_emit_2100_megatonne.iloc[5:8,0:5].sum(axis=1), color=colormats[5])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['backsheet'],
bottom=modmats_emit_2100_megatonne.iloc[5:8,0:6].sum(axis=1), color=colormats[6])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['module'],
bottom=modmats_emit_2100_megatonne.iloc[5:8,0:7].sum(axis=1), color='black')
ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.9', ls='--')
ax2.set_axisbelow(True)
#Ambitious
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['glass'], color=colormats[0])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['silicon'],
bottom=modmats_emit_2100_megatonne[8:]['glass'], color=colormats[1])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['silver'],
bottom=modmats_emit_2100_megatonne.iloc[8:,0:2].sum(axis=1), color=colormats[2])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['aluminium_frames'],
bottom=modmats_emit_2100_megatonne.iloc[8:,0:3].sum(axis=1), color=colormats[3])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['copper'],
bottom=modmats_emit_2100_megatonne.iloc[8:,0:4].sum(axis=1), color=colormats[4])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['encapsulant'],
bottom=modmats_emit_2100_megatonne.iloc[8:,0:5].sum(axis=1), color=colormats[5])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['backsheet'],
bottom=modmats_emit_2100_megatonne.iloc[8:,0:6].sum(axis=1), color=colormats[6])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['module'],
bottom=modmats_emit_2100_megatonne.iloc[8:,0:7].sum(axis=1), color='black')
ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.9', ls='--')
ax3.set_axisbelow(True)
#overall fig
fig_cumuemit_modmat.suptitle('Cumulative Emisisons in 2100 by material', fontsize=24)
plt.show()
#fig_cumuemit_modmat.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_10592\3009395773.py:23: UserWarning: FixedFormatter should only be used together with FixedLocator ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45) C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_10592\3009395773.py:45: UserWarning: FixedFormatter should only be used together with FixedLocator ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45) C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_10592\3009395773.py:68: UserWarning: FixedFormatter should only be used together with FixedLocator ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
#mins in 2050 and 2100
cumu_carbon_2050 = sim_cumu_carbon.loc[2050].filter(like='Annual_Emit_total_modmats')/1e12
cumu_carbon_2100 = sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats')/1e12
cumu_carbon_rankings_crittime = pd.concat([cumu_carbon_2050,cumu_carbon_2100], axis=1)
cumu_carbon_rankings_crittime.index = scennames_labels_flat
cumu_carbon_rankings_crittime
| 2050 | 2100 | |
|---|---|---|
| PV_ICE | 14058.924952 | 29635.654765 |
| PERC | 12794.376877 | 21268.446936 |
| SHJ | 12187.971284 | 20248.851865 |
| TOPCon | 12443.042440 | 20675.844681 |
| Low Quality | 15135.691476 | 30507.197634 |
| Long-Lived | 13425.138619 | 22746.697922 |
| High Eff | 10927.903114 | 30535.051343 |
| Circular | 17562.405463 | 27213.805311 |
| High Eff + Long-life | 11214.153373 | 22957.002686 |
| Long-Life + Recycling | 13438.818720 | 22649.094749 |
| Recycled-Si + Long-life | 14238.914154 | 22549.456487 |
| Circular + Long-life | 18287.841124 | 26533.880681 |
| Circular + High Eff | 15025.479472 | 22934.108827 |
cumu_carbon_rankings_crittime_plot = cumu_carbon_rankings_crittime.copy()
cumu_carbon_rankings_crittime_plot['diff'] = cumu_carbon_rankings_crittime[2100]-cumu_carbon_rankings_crittime[2050]
fig_cumulativeemit, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True,
gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(cumu_carbon_rankings_crittime_plot.index[0:5], cumu_carbon_rankings_crittime_plot[2050].iloc[0:5],
tick_label=scennames_labels[0:5], color=colorpalette[0:5], alpha = 0.7, edgecolor='white')
ax0.bar(cumu_carbon_rankings_crittime_plot.index[0:5], cumu_carbon_rankings_crittime_plot['diff'].iloc[0:5],
bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[0:5],
tick_label=scennames_labels[0:5], color=colorpalette[0:5])
ax0.set_ylim(0,31000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes CO2eq]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.9', ls='--')
ax0.set_axisbelow(True)
#Extreme
ax2.bar(cumu_carbon_rankings_crittime_plot.index[5:8], cumu_carbon_rankings_crittime_plot[2050].iloc[5:8],
tick_label=scennames_labels[5:8], color=colorpalette[5:8], alpha = 0.7, edgecolor='white')
ax2.bar(cumu_carbon_rankings_crittime_plot.index[5:8], cumu_carbon_rankings_crittime_plot['diff'].iloc[5:8],
bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[5:8],
tick_label=scennames_labels[5:8], color=colorpalette[5:8])
ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.9', ls='--')
ax2.set_axisbelow(True)
#Ambitious
ax3.bar(cumu_carbon_rankings_crittime_plot.index[8:], cumu_carbon_rankings_crittime_plot[2050].iloc[8:],
tick_label=scennames_labels[8:], color=colorpalette[8:], hatch='x', edgecolor='white', alpha=0.7)
ax3.bar(cumu_carbon_rankings_crittime_plot.index[8:], cumu_carbon_rankings_crittime_plot['diff'].iloc[8:],
bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[8:],
tick_label=scennames_labels[8:], color=colorpalette[8:], hatch='x', edgecolor='white')
ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.9', ls='--')
ax3.set_axisbelow(True)
#overall fig
fig_cumulativeemit.suptitle('Cumulative Emisisons in 2050, 2100', fontsize=24)
plt.show()
#fig_eBalance.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')
process_emissions = pd.DataFrame()
for scen in scenarios:
scen_p_totrim = sim_carbon_results.filter(like=scen).filter(like='_p_')
exclude_p_sums = ['_HQ_p_','_LQ_p_','_e_p_']
exclude_p_sums_search = '|'.join(exclude_p_sums)
scen_p = scen_p_totrim.loc[:,~scen_p_totrim.columns.str.contains(exclude_p_sums_search)]
scen_p_sum = scen_p.sum(axis=1)
process_emissions = pd.concat([process_emissions,scen_p_sum], axis=1)
process_emissions.columns = scennames_labels_flat
process_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
process_emissions_cumu = process_emissions.cumsum()
This is capturing steam and heating fuel
fuel_emissions = pd.DataFrame()
for scen in scenarios:
scen_f = sim_carbon_results.filter(like=scen).filter(like='_fuel_')
scen_f_sum = scen_f.sum(axis=1)
fuel_emissions = pd.concat([fuel_emissions,scen_f_sum], axis=1)
fuel_emissions.columns = scennames_labels_flat
fuel_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
fuel_emissions_cumu = fuel_emissions.cumsum()
both module and material level elec.
elec_emissions = pd.DataFrame()
for scen in scenarios:
scen_mod_elec = sim_annual_carbon.filter(like=scen).filter(like='Annual_Emit_mod') #module elec lifecycle energy
mat_eleckey = ['Global_vmfg_elec','landfill_elec','ReMFG_clean','Recycled_LQ','Recycled_HQ_elec']
mat_elecs_search = '|'.join(mat_eleckey)
scen_mat_elecs = sim_carbon_results.loc[:,sim_carbon_results.columns.str.contains(mat_elecs_search)].filter(like=scen)
#sum them together by scen
scen_mat_elecs_sum = scen_mat_elecs.sum(axis=1)
scen_elec_modmat_annual_sum = scen_mat_elecs_sum+scen_mod_elec.iloc[:,0]
elec_emissions = pd.concat([elec_emissions,scen_elec_modmat_annual_sum], axis=1)
elec_emissions.columns=scennames_labels_flat
elec_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
elec_emissions_cumu = elec_emissions.cumsum()
#(elec_emissions+fuel_emissions+process_emissions) vs sim_annual_carbon
#TO DO: I have a double counting or missing data problem somewhere, these should add to the same I think.
#graphing by emission source
efp_emit_total = elec_emissions+fuel_emissions+process_emissions
efp_emit_total.index
RangeIndex(start=2000, stop=2101, step=1)
#graphing by emission source, annual
#efp_emit_total = elec_emissions+fuel_emissions+process_emissions
for scen in scennames_labels_flat:
plt.plot([],[],color='black', label='process')
plt.plot([],[],color='darkred', label='fuel')
plt.plot([],[],color='blue', label='electricity')
plt.stackplot(elec_emissions.index,
process_emissions[scen]/1e12,
fuel_emissions[scen]/1e12,
elec_emissions[scen]/1e12,
colors = ['black','darkred','blue'])
plt.title(scen+':\nGHG Emissions Annually by Source')
plt.ylabel('GHG Emissions Annually from Lifecycle Source\n[million metric tonnes CO2eq]')
plt.xlim(2000,2100)
plt.ylim(0,)
handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.4,1))
#plt.legend()
plt.show()
#graphing by emission source, annual
for scen in scennames_labels_flat:
plt.plot([],[],color='black', label='process')
plt.plot([],[],color='darkred', label='fuel')
plt.plot([],[],color='blue', label='electricity')
plt.stackplot(elec_emissions_cumu.index,
process_emissions_cumu[scen]/1e12,
fuel_emissions_cumu[scen]/1e12,
elec_emissions_cumu[scen]/1e12,
colors = ['black','darkred','blue'])
plt.title(scen+':\nGHG Emissions Annually by Source')
plt.ylabel('GHG Emissions Annually from Lifecycle Source\n[million metric tonnes CO2eq]')
plt.xlim(2000,2100)
plt.ylim(0,maxy)
handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.4,1))
#plt.legend()
plt.show()